Acoustic adaptation is playing an increasingly important role in speech recognition systems, to compensate for the acoustic mismatch between training and test data, and also to adapt speaker-independent systems to individual speakers. Most speech recognition systems use acoustic models that include multi-dimensional gaussians that model the probability density function (pdf) of the feature vectors for different classes. (For general background on speech recognition, including gaussian mixture pdf's, see, e.g. Fundamentals of Speech Recognition, Lawrence Rabiner and Biing-Hwang Juang, Prentice Hall, 1993; and Statistical Methods for Speech Recognition, Frederick Jelinek, The MIT Press, 1997.) A commonly used adaptation technique in this connection is maximum likelihood linear regression (MLLR), which assumes that the parameters of the gaussians are transformed by an affine transform into parameters that better match the test or adaptation data. In a simple implementation, the mean ui of each gaussian gi is transformed according to ui′=Aui where A is the transform matrix, and ui is optionally padded with ones to represent an offset. The transform is chosen so as to maximize the probability of a collection of adaptation data with associated transcriptions. In more sophisticated implementations, the gaussian variances may also be adjusted. MLLR is further discussed, for instance, in Leggetter et al., “Speaker Adaptation of Continuous Density HMM's Using Multivariate Linear Regression”, Proceedings of ICSLP '94, Yokohama, Japan, 1994. This technique is also often used in “unsupervised” mode, where the correct transcription of the adaptation data is not known, and a first pass decoding using a speaker independent system is used to produce an initial transcription.
Although MLLR appears to work fairly well even when the unsupervised transcription is mildly erroneous, it is recognized herein that further improvements are possible.
Accordingly, a need has been recognized, inter alia, in connection with improving upon the shortcomings and disadvantages associated with conventional arrangements such as those discussed above.